
Transformer [1]} performs the explanation generation task by treating user and item IDs as words.
We also tested encoder-decoder Transformer, where the encoder encodes the IDs for the decoder to decode, but its results turned out to be the same, so we do not report it.

NRT [2]} can predict a rating and generate a tip simultaneously based on user and item IDs.
We take the explanations in the datasets as tips.
Moreover, we found that the model's problem of generating identical sentences (as reported in [3]}) is caused by the L2 regularization in its original design.
For fair comparison, we removed it.

Att2Seq [4]} is a review generation approach and we take the explanations as reviews.
This model has an attention module, but we found that it makes the generated content unreadable in the task.
To be fair, we removed it as well.

